pi0 (version 1.3-250)

sparncpt: Semiparametric density estimation for noncentrality parameters

Description

Semiparametric density estimation for noncentrality parameters using the combination method of Olkin and Spiegelman (1987), based on fits from both parncpt and nparncpt.

Usage

sparncpt(obj1, obj2, ...)
## S3 method for class 'parncpt':
sparncpt(obj1, obj2, ...)
## S3 method for class 'nparncpt':
sparncpt(obj1, obj2, ...)
## S3 method for class 'numeric':
sparncpt(obj1, obj2, ...)

Arguments

obj1, obj2

Case 1: obj1 and obj2 are of class parncpt and nparncpt respectively; or vice versa; Case 2: obj1 is a numeric ve

...

other arguments passed to dtn.mix, most notably the approximation argument.

Value

• a list with class c('sparncpt','ncpest'):
• pi0
  estimated proportion of true nulls
• mu.ncp
  mean of ncp
• sd.ncp
  SD of ncp
• logLik
  an object of class logLik. The associated df is the estimated effective number of parameters (enp). The log likelihood is also penalized likelihood. See also logLik.ncpest and AIC.
• enp
  estimated ENP
• par
  estimated mixing proportion rho
• gradiant
  analytic gradiant at the estimate (not implemented)
• hessian
  analytic hessian at the estimate (not implemented)
• parfit
  the fitted parncpt object
• nparfit
  the fitted nparncpt object

Details

This is a two-component mixture of a parametric fit from parncpt and a nonparametric fit from nparncpt, with mixing proportion rho. If obj1 and obj2 are t-statistics and degrees of freedom respectively, calls to each of parncpt and nparncpt are made and their results are used in combination.

References

I. Olkin and C. H. Spiegelman. (1987) A Semiparametric Approach to Density Estimation. Journal of the American Statistical Association. 82,399,858--865 Qu L, Nettleton D, Dekkers JCM. (2012) Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of $t$-statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis. Biometrics (in press).

See Also

parncpt, nparncpt, fitted.sparncpt, plot.sparncpt, summary.sparncpt, coef.ncpest, logLik.ncpest, vcov.ncpest, AIC, dncp

Examples

data(simulatedTstat)
(npfit=nparncpt(tstat=simulatedTstat, df=8)); 
(pfit=parncpt(tstat=simulatedTstat, df=8, zeromean=FALSE)); plot(pfit)
(pfit0=parncpt(tstat=simulatedTstat, df=8, zeromean=TRUE)); plot(pfit0)
(spfit=sparncpt(npfit,pfit)); plot(spfit)